[](https://pipekit.io)
[Pipekit](https://pipekit.io) allows you to manage your workflows at scale. The control plane configures Argo Workflows for you in your infrastructure, enabling you to optimize multi-cluster workloads while reducing your cloud spend. The team at Pipekit is also happy to support you through your Argo Workflows journey via commercial support.
# Pipekit Python SDK
## Installation
```bash
pip install pipekit-sdk
```
## Usage
```py
# The Pipekit SDK interacts with Hera Workflows classes
from hera.workflows import Container, Step, Steps, Workflow, script
from pipekit_sdk.service import PipekitService
# Create a Pipekit service that is used to talk to the Pipekit API
pipekit = PipekitService(token="<token>")
# List clusters and Pipes
clusters = pipekit.list_clusters()
pipes = pipekit.list_pipes()
@script()
def flip_coin() -> None:
import random
result = "heads" if random.randint(0, 1) == 0 else "tails"
print(result)
# Create a Workflow using Hera
with Workflow(
generate_name="coinflip-",
annotations={
"workflows.argoproj.io/description": (
"This is an example of coin flip defined as a sequence of conditional steps."
),
},
entrypoint="coinflip",
namespace="argo",
service_account_name="argo",
) as w:
heads = Container(
name="heads",
image="alpine:3.6",
command=["sh", "-c"],
args=['echo "it was heads"'],
)
tails = Container(
name="tails",
image="alpine:3.6",
command=["sh", "-c"],
args=['echo "it was tails"'],
)
with Steps(name="coinflip") as s:
fc: Step = flip_coin()
with s.parallel():
heads(when=f"{fc.result} == heads")
tails(when=f"{fc.result} == tails")
# Submit the Workflow to Pipekit
pipekit.submit(w, "<cluster-name>")
# Tail the logs
pipekit.print_logs(pipe_run.uuid)
```
## Further help
Please refer to the [Pipekit Documentation](https://docs.pipekit.io) for more information.
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"description": "[](https://pipekit.io)\n\n[Pipekit](https://pipekit.io) allows you to manage your workflows at scale. The control plane configures Argo Workflows for you in your infrastructure, enabling you to optimize multi-cluster workloads while reducing your cloud spend. The team at Pipekit is also happy to support you through your Argo Workflows journey via commercial support.\n\n# Pipekit Python SDK\n\n## Installation\n\n```bash\npip install pipekit-sdk\n```\n\n## Usage\n\n```py\n# The Pipekit SDK interacts with Hera Workflows classes\nfrom hera.workflows import Container, Step, Steps, Workflow, script\nfrom pipekit_sdk.service import PipekitService\n\n# Create a Pipekit service that is used to talk to the Pipekit API\npipekit = PipekitService(token=\"<token>\")\n\n# List clusters and Pipes\nclusters = pipekit.list_clusters()\npipes = pipekit.list_pipes()\n\n@script()\ndef flip_coin() -> None:\n import random\n\n result = \"heads\" if random.randint(0, 1) == 0 else \"tails\"\n print(result)\n\n# Create a Workflow using Hera\nwith Workflow(\n generate_name=\"coinflip-\",\n annotations={\n \"workflows.argoproj.io/description\": (\n \"This is an example of coin flip defined as a sequence of conditional steps.\"\n ),\n },\n entrypoint=\"coinflip\",\n namespace=\"argo\",\n service_account_name=\"argo\",\n) as w:\n heads = Container(\n name=\"heads\",\n image=\"alpine:3.6\",\n command=[\"sh\", \"-c\"],\n args=['echo \"it was heads\"'],\n )\n tails = Container(\n name=\"tails\",\n image=\"alpine:3.6\",\n command=[\"sh\", \"-c\"],\n args=['echo \"it was tails\"'],\n )\n\n with Steps(name=\"coinflip\") as s:\n fc: Step = flip_coin()\n\n with s.parallel():\n heads(when=f\"{fc.result} == heads\")\n tails(when=f\"{fc.result} == tails\")\n\n# Submit the Workflow to Pipekit\npipekit.submit(w, \"<cluster-name>\")\n\n# Tail the logs\npipekit.print_logs(pipe_run.uuid)\n```\n\n## Further help\nPlease refer to the [Pipekit Documentation](https://docs.pipekit.io) for more information.\n\n",
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